Machine Vision-Based Method and System to Facilitate the Unloading of a Pile of Cartons in a Carton Handling System

ABSTRACT

A machine vision-based method and system to facilitate the unloading of a pile of cartons within a work cell are provided. The method includes the step of providing at least one 3-D or depth sensor having a field of view at the work cell. Each sensor has a set of radiation sensing elements which detect reflected, projected radiation to obtain 3-D sensor data. The 3-D sensor data including a plurality of pixels. For each possible pixel location and each possible carton orientation, the method includes generating a hypothesis that a carton with a known structure appears at that pixel location with that container orientation to obtain a plurality of hypotheses. The method further includes ranking the plurality of hypotheses. The step of ranking includes calculating a surprisal for each of the hypotheses to obtain a plurality of surprisals. The step of ranking is based on the surprisals of the hypotheses.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application to co-pendingpatent application Ser. No. 17/020,910 filed Sep. 15, 2020. Thatapplication is a continuation application of patent application Ser. No.16/174,554, filed Oct. 30, 2018.

TECHNICAL FIELD

At least one embodiment of the present invention generally relates tomachine vision-based methods and systems to facilitate the unloading ofa pile or stack of cartons or boxes in a material handling system.

OVERVIEW

In certain applications it is desirable to move objects from one palletor platform and place them into another pallet, tote, platform, orconveyance for reassembly and further processing. These objects aretypically boxes of varying volumes and weights that must be placed intoa receptacle or conveyor line based on a set of rules such as: size ofobject, size of destination tote or conveyance. Additional rules may beinferred based on printed material on the box, or additionally the kindof box. Box types can vary widely including partial openings in a boxtop, shape of the box top, whether or not the box is plain cardboard orhas been printed. In material handling there are several processes formoving these kinds of objects. In a manual single box pick process,manual operators are presented an assembly of box-like objects andselect an individual object to move from a plane or other conveyance toa tote or other conveyance for further processing. In an automatedsingle box pick process the box handling is typically performed by arobot.

A decanting process builds on the single box picking process. Again,typically, manual labor is used to “decant” objects from a plane ofobjects. These objects are a set of box-like objects, that may or maynot be adjacent to each other, that must be moved onto a conveyance ortote, either singly or as a group.

The pose of an object is the position and orientation of the object inspace relative to some reference position and orientation. The locationof the object can be expressed in terms of X, Y, and Z. The orientationof an object can be expressed in terms of Euler angles describing itsrotation about the x-axis (hereinafter RX), rotation about the y-axis(hereinafter RY), and then rotation about the Z-axis (hereinafter RZ)relative to a starting orientation. There are many equivalent mathematiccoordinate systems for designating the pose of an object: positioncoordinates might be expressed in spherical coordinates rather than inCartesian coordinates of three mutually perpendicular axes; rotationalcoordinates may be express in terms of quaternions rather than Eulerangles; 4×4 homogeneous matrices may be used to combine position androtation representations; etc. But generally, six variables X, Y, Z, RX,RY, and RZ suffice to describe the pose of a rigid object in 3D space.

Automated single box pick and decanting have some clear issues thathumans can easily overcome. For instance, a human might recognize easilythat a box position is tipped, rotated, or otherwise not be in a presetlocation on the plane of boxes. Additionally, a human may easily seethat only so many box objects can be moved at a time. Humans also wouldbe able to quickly understand if one object were overlapping another andbe able to still move the objects.

Unfortunately, both of the above-noted manual processes are repetitiveand prone to burn out and injury for these workers. Manufacturers mightalso want to move these humans into more appropriate places to superviseautomation or other important duties. Ideally, a processing plant wouldwant to automate these processes thus reducing injury, labor shortageand to apply certain rules to the boxes.

In existing art, automated single box picking, or decanting requirepre-known information about the arrangement and location of boxesthrough pre-defined parameters. These parameters must be setup inadvance and do not allow for simple changes, or the introduction of newboxes without training or configuration. Other configurations rely onbarcoding, or other identification methods that rely on known data aboutthe boxes to determine location and size of boxes.

The automation may include the introduction of robotic systems, sensors,conveyors, or other automated techniques to improve object processing.There is no prior art that handles these kinds of systems without largecost in development and training or significant maintenance in addingnew products.

These existing systems rely on either Classical Image Processing orMachine Learning are described hereinbelow.

A. Classical Image Processing

Classical Image Processing is reliant on a dataset and is dependent onFeature Extraction, Segmentation and Detection to obtain informationregarding an item of interest as shown in FIG. 1.

Feature extraction attempts to locate and extract features from theimage. Segmentation processes use the extracted features to separate theforeground from the background to isolate the portion of an image withdesirable data. The processes of feature extraction and segmentation mayiterate to extract a final set of features for use in the detectionphase. In the final detection phase, a classification, objectrecognition or measurement is given based on the features in theforeground.

Additionally, existing art requires systems to learn about reading printin a separate process that adds additional time and effort to detectionof the desired objects.

Lastly, classical image processing depends on parameters that may bearbitrarily selected such as a percentage of depth and greyscale todetermine features—which is error fraught. These kinds of additionalparameters add complexity and error to the process.

B. Machine Learning

Alternatively, other systems that automate single box picking ordecanting rely on some form of Machine Learning (ML) principles. Theseare a modern approach to interacting with unknown materials and canlearn to classify objects based on a training set of data. Essentiallythese systems go through a process of identifying objects and asking ifthe result matches desired result (training). This process requires alarge dataset and consumes a significant amount of time.

There are a couple of defects of this process: overtraining and durationof training.

The system must discover features during the training phase and look forthings that are important to calculate about the images. Some of thesefeatures might include edge or color, or depth or gradients or someother feature that is known only to the training process. Humans gatherthis information by parallax. An additional downfall of this method isthat the more types of input to the ML the longer the training phase.

Too little training on a ML will mean that the system does not havesufficient data for a trained set. Too much and the dataset will beoversized and degrade performance. A balance between the two is requiredand dependent on a holdout dataset to validate sufficient data.

An additional issue with ML training is accounting for new objects to beadded into the process. Anytime a new object is introduced, or anexisting object is changed, the system must be retrained for the newdata.

The following U.S. patent publications are related to at least oneembodiment of the present invention: 2016/0221187; 2018/0061043;2019/0262994; 2020/0086437; 2020/0134860; 2020/0234071; U.S. Pat. Nos.9,493,316; 9,630,320; 9,630,321; 10,239,701; 10,315,866; and 10,662,007.

SUMMARY OF EXAMPLE EMBODIMENTS

An object of at least one embodiment of the present invention is toprovide a machine vision-based method and system which overcome theabove-noted shortcomings of Classical Image Processing and/or ML toprovide a faster, more reliable process and system.

Other objects of at least one embodiment of the present invention are toprovide a machine vision-based method and system which:

-   -   Eliminate the time for training;    -   Eliminate the possibility of over or undertraining;    -   Eliminate the need to collect hundreds or thousands of samples        for training;    -   Eliminate the need for retraining;    -   Speed up the process of identifying an item of interest;    -   Read printed boxes and unprinted boxes in the same framework;        and/or    -   Unify all the sources of information about the item of interest        without using arbitrarily selected parameters.

In carrying out the above objects and other objects of at least oneembodiment of the present invention, a machine vision-based method tofacilitate the unloading of a pile of cartons within a work cell in anautomated carton handling system is provided. The method includes thestep of providing at least one 3-D or depth sensor having a field ofview at the work cell. The at least one sensor has a set of radiationsensing elements which detect reflected, projected radiation to obtain3-D sensor data. The 3-D sensor data includes a plurality of pixels. Foreach possible pixel location and each possible carton orientation, themethod includes generating a hypothesis that a carton with a knownstructure appears at that pixel location with that container orientationto obtain a plurality of hypotheses. The method further includes rankingthe plurality of hypotheses. The step of ranking includes calculating asurprisal for each of the hypotheses to obtain a plurality ofsurprisals. The step of ranking is based on the surprisals of thehypotheses. At least one carton of interest is unloaded from the pilebased on the ranked hypotheses.

The method may further include utilizing an approximation algorithm tounload a plurality of cartons at a time from the pile in a minimumnumber of picks.

The work cell may be a robot work cell.

The sensor may be a hybrid 2-D/3-D sensor.

The sensor may include a pattern emitter for projecting a known patternof radiation and a detector for detecting the known pattern of radiationreflected from a surface of the carton.

The pattern emitter may emit a non-visible pattern of radiation and thedetector may detect the reflected non-visible pattern of radiation.

The sensor may be a volumetric sensor capable of capturing thousands ofindividual points in space.

At least one of the hypotheses may be based on print on at least one ofthe cartons.

Further in carrying out the above objects and other objects of at leastone embodiment of the present invention, a machine vision-based systemto facilitate the unloading of a pile of cartons within a work cell inan automated carton handling system is provided. The system includes atleast one 3-D or depth sensor having a field of view at the work cell.The at least one sensor has a set of radiation sensing elements whichdetect reflected, projected radiation to obtain 3-D sensor data. The 3-Dsensor data including a plurality of pixels. The system also includes atleast one processor to process the 3-D sensor data and, for eachpossible pixel location and each possible carton orientation, generate ahypothesis that a carton with a known structure appears at that pixellocation with that container orientation to obtain a plurality ofhypotheses. The at least one processor ranks the plurality ofhypotheses. Ranking includes calculating a surprisal for each of thehypotheses to obtain a plurality of surprisals. Ranking is based on thesurprisals of the hypotheses. The system further includes avision-guided robot for unloading at least one carton of interest fromthe pile based on the ranked hypotheses.

The at least one processor may utilize an approximation algorithm sothat the vision-guided robot unloads a plurality of cartons at a timefrom the pile in a minimum number of picks.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram flow chart which illustrates classical imageprocessing;

FIG. 2 is a block diagram flow chart which illustrates at least oneembodiment of a machine vision-based method of the present invention;

FIG. 3 is a graph of a probability histogram to determine theprobability of observing a pixel with intensity g(h,v) and a top imageof a box-like object;

FIG. 4 is a graph of a cumulative probability histogram to determine thecumulative probability of seeing a pixel this bright or brighter, anequation for cumulative distribution function (CDF) and a top imagesimilar to the image of FIG. 3;

FIG. 5 is a top image illustrating random pixel selection;

FIG. 6 illustrates a cumulative probability formula;

FIG. 7 is a top image similar to the images of FIGS. 3 and 4 with arrowsto identify sharp edges in the image;

FIG. 8 illustrates a structure formula;

FIG. 9 is a top image similar to the images of FIGS. 3 and 4 with arrowsto identify various surface points;

FIG. 10 is a top image similar to the images of FIGS. 3 and 4 whereinline segment detection may be performed;

FIG. 11 is a block diagram flow chart similar to the chart of FIG. 2 fora box likelihood evaluation algorithm;

FIG. 12 is a schematic diagram which illustrates the select or selectionstep of FIG. 11;

FIG. 13 is a schematic diagram which illustrates a multi-box parsealgorithm;

FIG. 14 are schematic diagrams which illustrate legal picks in a palletdecomposition algorithm;

FIG. 15 are schematic diagrams which illustrate illegal picks in thepallet decomposition algorithm; and

FIG. 16 is a schematic block diagram of a system constructed inaccordance with at least one embodiment of the present invention.

DETAILED DESCRIPTION

As required, detailed embodiments of the present invention are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely exemplary of the invention that may be embodied in variousand alternative forms. The figures are not necessarily to scale; somefeatures may be exaggerated or minimized to show details of particularcomponents. Therefore, specific structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as arepresentative basis for teaching one skilled in the art to variouslyemploy the present invention.

Preferably, one or more 3-D or depth sensors 32 (FIG. 16) of at leastone embodiment of the invention measure distance via massively paralleltriangulation using a projected pattern (a “multi-point disparity”method). The specific types of active depth sensors which are preferredare called multipoint disparity depth sensors.

“Multipoint” refers to the laser projector which projects thousands ofindividual beams (aka pencils) onto a scene. Each beam intersects thescene at a point.

“Disparity” refers to the method used to calculate the distance from thesensor to objects in the scene. Specifically, “disparity” refers to theway a laser beam's intersection with a scene shifts when the laser beamprojector's distance from the scene changes.

“Depth” refers to the fact that these sensors are able to calculate theX, Y and Z coordinates of the intersection of each laser beam from thelaser beam projector with a scene.

“Passive Depth Sensors” determine the distance to objects in a scenewithout affecting the scene in any way; they are pure receivers.

“Active Depth Sensors” determine the distance to objects in a scene byprojecting energy onto the scene and then analyzing the interactions ofthe projected energy with the scene. Some active sensors project astructured light pattern onto the scene and analyze how long the lightpulses take to return, and so on. Active depth sensors are both emittersand receivers.

For clarity, each sensor 32 is preferably based on active monocular,multipoint disparity technology as a “multipoint disparity” sensorherein. This terminology, though serviceable, is not standard. Apreferred monocular (i.e. a single infrared camera) multipoint disparitysensor is disclosed in U.S. Pat. No. 8,493,496. A binocular multipointdisparity sensor, which uses two infrared cameras to determine depthinformation from a scene, is also preferred.

Multiple volumetric sensors 32 may be placed in key locations around andabove the piles or stacks of cartons 25 (FIG. 16). Each of these sensors32 typically captures hundreds of thousands of individual points inspace. Each of these points has a Cartesian position in space. Beforemeasurement, each of these sensors 32 is registered into a commoncoordinate system. This gives the present system the ability tocorrelate a location on the image of a sensor with a real worldposition. When an image is captured from each sensor 32, the pixelinformation, along with the depth information, is converted by acomputer into a collection of points in space, called a “point cloud”.

In general, sources of information are unified in the same framework, sothat they can be compared as commensurate quantities without usingspecial parameters. This approach to image processing is generally notedas follows: Generate hypotheses. Rank how well each hypothesis matchesthe evidence, then select the ‘best’ hypothesis as the answer. Thisapproach is very probabilistic in nature and is shown in the blockdiagram flow chart of FIG. 2.

Boxes or cartons as an example: What are the hypotheses in boxes?

-   -   A box is located at some h,v position with some orientation        (h,v,r). For every position and possible orientation, a        hypothesis is generated.    -   Ranking the hypotheses. For each hypothesis one calculates how        improbable that the configuration arose by chance. One        calculates probability to see if something shows up by random        chance.

Explanation of Calculation

Focus on intensity in the image and look just for the edge (strength ofthe edge). What is the probability that there is a pixel that bright?One could focus on the brightest pixels and calculate the probability ofa histogram (i.e. probability histogram of FIG. 3) and normalize, whichis not particularly useful.

One asks if there is a pixel with brightness equal to or greater thanthis pixel, which is a Cumulative Distribution Function (see thecumulative probability histogram of FIG. 4). Cumulative DistributionFunctions, which are integrals of histograms, gives one the ability toobserve the probability of one pixel. One looks at a histogram of one ormore images and assign a probability to one particular pixel.

Then the probability of observing a pixel with intensity greater than orequal to the given value is 1-CDF(g).

If one pick pixels at random (see FIG. 5) from a histogram, one cannotget structure and will have just noise.

What is the probability of observing a pixel this bright or brighter?See the cumulative probability formula of FIG. 6.

If one takes the multiplication over the box, one can hypothesize wherethe box is and the orientation and do it for only one box. For eachpoint and rotation of the image one can assign probability of the boxbeing there. It requires a lot of computation to get this number. Seethe sharp edges of the box of FIG. 7.

What is the probability of seeing all the pixels in that box together bychance? Assuming independent probabilities, the probability of observinga whole bunch of independent events together is the multiplication ofthe probabilities of the individual events. Pi means multiply all thesethings together. This quantity is the multiplication of a bunch of verysmall numbers. A low number means this configuration will not occur byrandom chance.

${p\left( {{that}\mspace{14mu} {box}} \right)} = {\prod\limits_{{g{({h,v})}} \in {{that}\mspace{14mu} {box}}}{p\left( {{g\left( {h,v} \right)} \geq G} \right)}}$

One does not do all the multiplication computations because they arenumerically unstable. One calculates the entropy, (aka the surprisal),which is the negative log probability of the number or observation.Negative log is entropy, which allows for addition instead ofmultiplication, therefore one works in surprisal which makes probabilitymore accurate and faster. (See FIG. 8)

One could just as easily have done this same thing working on theintensity image, volume, or any other feature like print, rather thanthe edge image. The algorithm does not care what features one is lookingat.

The distribution of the random variable, G, is found by observation. Thealgorithm is good enough that observation of a single image issufficient, but by continuously updating the CDF as we go, theperformance of the algorithm improves

Using classical methods, if one were looking at the surface points ofFIG. 9, one might use a morphological structuring element the size ofthe box and perform some morphology and then threshold to a binaryimage, then perform connected components to look for something of thecorrect shape.

If one were to look at FIG. 10, one might perform some line segmentdetections, perhaps using a Hough transform then a threshold, then tryto assemble a box from the line segments. Note the threshold parameters.At least one embodiment of the present invention eliminates theparameters in favor of a maximum computation on the entropy image: asidefrom the structuring model of FIG. 8, there are no parameters.

Also, consider what happens if one uses grayscale gradients, but thenone wants to add depth gradients as a second source of information. Howdoes one add these quantities together? The classical approach to imageprocessing has no answer for this question. Only the present approach ofthis application has a ready answer: multiply the probabilities (add theSurprisals). In this approach one could use a whole image, or depthimage, or greyscale, or any image.

Algorithm 1: Box Likelihood Evaluation

-   -   Generate: For each pixel (h,v), for each box orientation a=0-359        degrees of (h,v), generate a hypothesis H(h,v,a) that a box with        dimensions L×W appears at that location, with that orientation.    -   Rank: Compute the surprisal for each hypothesis. Rank the        hypotheses according to Surprisals. Bigger surprisal means it is        a better chance box or organization of chance value.    -   Select: Get the best hypothesis. (Single box pick) Very unlikely        to see a tie.

Algorithm 2: Multibox Parse

Decanting: In Multibox Parse, one does not do the select phase, one doesnot care about the best box. One needs to know where all the boxes are.Visually, the Surprisal hypothesis is represented in FIG. 12 by size andorientation of the oval.

For Single Box Pick one simply picks the strongest hypothesis from FIG.12.

For Multibox Parse: one must take the same information and find all theboxes.

-   -   Select a set of hypotheses to look for consistence. Boxes need        to be disjoint, cannot overlap.    -   Sum of the suprisals of the hypothesis is maximum.    -   Hypotheses that are consistent and maximum.    -   This is an NP hard problem. No known polynomial time solution;        cannot verify in polynomial time.    -   One can find an approximate solution in polynomial time.

One solves with an approximation method like Simulated Annealingalgorithm but multiple methods for approximating the optimum answer willpresent themselves.

Algorithm 3: Pallet Decomposition

The algorithm for Pallet Decomposition will ideally partition a layer ofboxes such that the number of partitions is minimal—one wants to emptythe layer of boxes, by picking multiple boxes at a time, in the minimumnumber of picks.

Decomp Sequence of legal picks to empty a layer.

Optimal decomposition is a decomposition with minimum number of picks.

Find an optimum decomposition for a layer.

NP Hard, so we will be using approximation like Branch and Boundalgorithm.

A legal pick does not overlap existing boxes. Pick tool does not overlayany box partially.

Illegal picks have tool picking overlapping boxes.

Algorithm 4: Reading Printed Data

-   -   Look at the boxes on the outside of the pallet (especially boxes        in corners) that have strong signals in both the edge grayscale        gradient and depth gradient surprisal matrices.    -   Boxes on the corners are identifiable by just the depth and edge        grayscale surprisal matrices.    -   Once one has identified the corner boxes using the gradient        information, one can ‘look at’ the print on corner boxes—that is        one can segregate visual data comprising the image of the print        from the visual data generated by the edges of the boxes.

Features of at Least One Embodiment of the Invention

1) Calculate Maximum Likelihood through surprisal. Use surprisal tounify treatment of all sources of information.

2) Describe HDLS using multipoint disparity sensors such as Kinectsensors available from Microsoft Corporation. Since one combinesgrayscale and depth in common probabilistic framework, it is importantto insure steadiness of distributions. One wants isolation from ambientillumination, so find a source to overwhelm ambient. Efficiency isobtained by using the IR sensors twice: once for disparity and once forgrayscale. Each sensor is configured to alternate between acquisition ofdisparity (depth) and grayscale information. Thus, one uses the samehardware for two purposes. The wavelength of disparity sensors operatesat frequency of fabret-perot IR laser at 830 nm. LED and laser diodesources are commercially available at 850 nm but not 830 nm. One usesspecial source at 850 nm, along with wide band pass filter between 830(disparity) and 850 HDLS.

3) No/Eliminate training. Use known structure of boxes to eliminatetraining session for ML Use of orthogonal projection allows one to treatall boxes the same. Use length, width, depth for grayscale and depthinformation. No matter how far away the boxes are or orientation, withorthogonal projection one knows that it is a box without the need fortraining.

4) Use gradient image of printed box. Use as additional information toimprove the likelihood of correctly identifying boxes on the interior ofthe pallet which may not have significant depth gradient because theyare packed together.

The system includes vision-guided robots 21 and one or more cameras 32having a field of view 30. The cameras 32 and the robots 21 may bemounted on support beams of a support frame structure of the system 10or may rest on a base. One of the cameras 32 may be mounted on one ofthe robots 21 to move therewith.

The vision-guided robots 21 have the ability to pick up any part withina specified range of allowable cartons using multiple-end-of-arm toolingor grippers. The robots pick up the cartons and orient them at aconveyor or other apparatus. Each robot 21 precisely positionsself-supporting cartons on a support or stage.

The robots 21 are preferably six axis robots. Each robot 21 isvision-guided to identify, pick, orient, and present the carton so thatthey are self-supporting on the stage. The grippers 17 accommodatemultiple part families.

Benefits of Vision-based Robot Automation include but are not limited tothe following:

Smooth motion in high speed applications;

Handles multiple cartons in piles 25 of cartons;

Slim designs to operate in narrow spaces;

Integrated vision; and

Dual end-of-arm tooling or grippers 17 designed to handle multiplecarton families.

A master control station or system controller (FIG. 16) determineslocations and orientations of the cartons or boxes in the pile or stackof cartons using any suitable machine vision system having at least onecamera (i.e. camera 32). Any one or more of various arrangements ofvision systems may be used for providing visual information from imageprocessors (FIG. 16) to the master controller. In one example, thevision system includes two three-dimensional cameras 32 that providesinfrared light over fields of vision or view 30. In various embodiments,the light may be infrared.

In some embodiment, multiple cameras such as the cameras 32 can besituated at fixed locations on the frame structure at the station, ormay be mounted on the arms of the robot 21. Two cameras 32 may be spacedapart from one another on the frame structure. The cameras 32 areoperatively connected to the master controller via their respectiveimage processors. The master controller also controls the robots of thesystem through their respective robot controllers. Based on theinformation received from the cameras 32, the master controller thenprovides control signals to the robot controllers that actuate roboticarm(s) or the one or more robot(s) 21 used in the method and system.

The master controller can include a processor and a memory on which isrecorded instructions or code for communicating with the robotcontrollers, the vision systems, the robotic system sensor(s), etc. Themaster controller is configured to execute the instructions from itsmemory, via its processor. For example, master controller can be hostmachine or distributed system, e.g., a computer such as a digitalcomputer or microcomputer, acting as a control module having a processorand, as the memory, tangible, non-transitory computer-readable memorysuch as read-only memory (ROM) or flash memory. The master controllercan also have random access memory (RAM), electrically-erasable,programmable, read only memory (EEPROM), a high-speed clock,analog-to-digital (A/D) and/or digital-to-analog (D/A) circuitry, andany required input/output circuitry and associated devices, as well asany required signal conditioning and/or signal buffering circuitry.Therefore, the master controller can include all software, hardware,memory, algorithms, connections, sensors, etc., necessary to monitor andcontrol the vision subsystem, the robotic subsystem, etc. As such, acontrol method can be embodied as software or firmware associated withthe master controller. It is to be appreciated that the mastercontroller can also include any device capable of analyzing data fromvarious sensors, comparing data, making the necessary decisions requiredto control and monitor the vision subsystem, the robotic subsystem,sensors, etc.

An end effector on the robot arm may include a series of gripperssupported to pick up the cartons. The robotic arm is then actuated byits controller to pick up the cartons with the particular gripper,positioning the gripper 17 relative to the cartons using the determinedlocation and orientation from the visual position and orientation dataof the particular vision subsystem including its camera and imageprocessor.

In general, the method and system of at least one embodiment of thepresent invention searches for objects like boxes or cartons which havehigh variability in shape, size, color, printing, barcodes, etc. Thereis lots of differences between each object, even of the same type andone needs to determine location of the boxes that may be jammed veryclose together, without much discernible feature. The method combinesboth 2D and 3D imaging (grayscale and depth) to get individuation of theobjects. The objects may all “look” the same to a human, but have highvariability between each assembled box or carton.

While exemplary embodiments are described above, it is not intended thatthese embodiments describe all possible forms of the invention. Rather,the words used in the specification are words of description rather thanlimitation, and it is understood that various changes may be madewithout departing from the spirit and scope of the invention.Additionally, the features of various implementing embodiments may becombined to form further embodiments of the invention.

What is claimed is:
 1. A machine vision-based method to facilitate theunloading of a pile of cartons within a work cell in an automated cartonhandling system, the method comprising the steps of: providing at leastone 3-D or depth sensor having a field of view at the work cell, the atleast one sensor having a set of radiation sensing elements which detectreflected, projected radiation to obtain 3-D sensor data, the 3-D sensordata including a plurality of pixels; for each possible pixel locationand each possible carton orientation, generating a hypothesis that acarton with a known structure appears at that pixel location with thatcontainer orientation to obtain a plurality of hypotheses; ranking theplurality of hypotheses wherein the step of ranking includes calculatinga surprisal for each of the hypotheses to obtain a plurality ofsurprisals and wherein the step of ranking is based on the surprisals ofthe hypotheses; and unloading at least one carton of interest from thepile based on the ranked hypotheses.
 2. The method as claimed in claim1, further comprising utilizing an approximation algorithm to unload aplurality of cartons at a time from the pile in a minimum number ofpicks.
 3. The method as claimed in claim 1, wherein the work cell is arobot work cell.
 4. The method as claimed in claim 1, wherein the atleast one sensor is a hybrid 2-D/3-D sensor.
 5. The method as claimed inclaim 1, wherein the at least one sensor includes a pattern emitter forprojecting a known pattern of radiation and a detector for detecting theknown pattern of radiation reflected from a surface of the carton. 6.The method as claimed in claim 5, wherein the pattern emitter emits anon-visible pattern of radiation and the detector detects the reflectednon-visible pattern of radiation.
 7. The method as claimed in claim 1,wherein the at least one sensor is at least one volumetric sensorcapable of capturing thousands of individual points in space.
 8. Themethod as claimed in claim 1, wherein at least one of the hypotheses isbased on print on at least one of the cartons.
 9. A machine vision-basedsystem to facilitate the unloading of a pile of cartons within a workcell in an automated carton handling system, the system comprising: atleast one 3-D or depth sensor having a field of view at the work cell,the at least one sensor having a set of radiation sensing elements whichdetect reflected, projected radiation to obtain 3-D sensor data, the 3-Dsensor data including a plurality of pixels; at least one processor toprocess the 3-D sensor data and, for each possible pixel location andeach possible carton orientation, generate a hypothesis that a cartonwith a known structure appears at that pixel location with thatcontainer orientation to obtain a plurality of hypotheses; the at leastone processor ranking the plurality of hypotheses wherein the rankingincludes calculating a surprisal for each of the hypotheses to obtain aplurality of surprisals and wherein the ranking is based on thesurprisals of the hypotheses; and a vision-guided robot for unloading atleast one carton of interest from the pile based on the rankedhypotheses.
 10. The system as claimed in claim 9, wherein the at leastone processor utilizes an approximation algorithm so that thevision-guided robot unloads a plurality of cartons at a time from thepile in a minimum number of picks.
 11. The system as claimed in claim 9,wherein the work cell is a robot work cell.
 12. The system as claimed inclaim 9, wherein the at least one sensor is a hybrid 2-D/3-D sensor. 13.The system as claimed in claim 9, wherein the at least one sensorincludes a pattern emitter for projecting a known pattern of radiationand a detector for detecting the known pattern of radiation reflectedfrom a surface of the carton.
 14. The system as claimed in claim 13,wherein the pattern emitter emits a non-visible pattern of radiation andthe detector detects the reflected non-visible pattern of radiation. 15.The system as claimed in claim 9, wherein the at least one sensor is avolumetric sensor capable of capturing thousands of individual points inspace.
 16. The system as claimed in claim 9, wherein at least one of thehypotheses is based on print on at least one of the cartons.